LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution

arXiv:2606.28335 · cs.CY, cs.AI, cs.CL · Submitted 2026-05-26 · Read on arXiv

cs.CY, cs.AI, cs.CL

Submitted: 2026-05-26

Updated: 2026-09-10

Comments: Accepted in Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2026), 43 pages, 18 figures, 17 tables

Code: https://github.com/sakhadib/LLM-Ideoplasticity

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

The gist: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution P(position context) over a real political space.

Terminology

Abstract

We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution P(position context) over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA-CHES projection models, which map responses onto three validated dimensions (lrgen, lrecon, galtan) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under-represented languages displace coordinates by up to 0.57 and 0.52 units, respectively, while chain-of-thought reasoning often amplifies rather than dampens paraphrase instability. Despite this local plasticity, the model cohort occupies a remarkably narrow Overton envelope overall, occupying roughly one-third the spread of major European parties. Supported by a multi-trait multi-method (MTMM) analysis, we conclude that a single point cannot summarize LLM political behavior; it must be characterized as a shape. Our code and data are publicly available at https://github.com/sakhadib/LLM-Ideoplasticity.

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